Preprint Review Version 2 Preserved in Portico This version is not peer-reviewed

Statistical Considerations for the Design and Analysis of Pragmatic Trials in Aging Research

Version 1 : Received: 7 May 2024 / Approved: 7 May 2024 / Online: 7 May 2024 (15:36:52 CEST)
Version 2 : Received: 4 June 2024 / Approved: 4 June 2024 / Online: 4 June 2024 (11:07:06 CEST)

A peer-reviewed article of this Preprint also exists.

Kammar-García, A.; Fernández-Urrutia, L.A.; Guevara-Díaz, J.A.; Mancilla-Galindo, J. Statistical Considerations for the Design and Analysis of Pragmatic Trials in Aging Research. Geriatrics 2024, 9, 75. Kammar-García, A.; Fernández-Urrutia, L.A.; Guevara-Díaz, J.A.; Mancilla-Galindo, J. Statistical Considerations for the Design and Analysis of Pragmatic Trials in Aging Research. Geriatrics 2024, 9, 75.

Abstract

Pragmatic trials aim to assess intervention efficacy in usual patient care settings, contrasting with explanatory trials conducted under controlled conditions. In aging research, pragmatic trials are important designs for obtaining real-world evidence in elderly populations, which are often underrepresented in trials. In this review, we discuss statistical considerations from a frequentist approach for the design and analysis of pragmatic trials. When choosing the dependent variable, it is essential to use an outcome that is highly relevant to usual medical care while also providing sufficient statistical power. Besides traditionally used binary outcomes, ordinal outcomes can provide pragmatic answers with gains in statistical power. Cluster randomization requires careful consideration of sample size calculation and analysis methods, especially regarding missing data and outcome variables. Mixed effects models and generalized estimating equations (GEEs) are recommended for analysis to account for center effects, with tools available for sample size estimation. Multi-arm studies pose challenges in sample size calculation, requiring adjustment for design effects and consideration of multiple comparison correction methods. Secondary analyses are common but require caution due to the risk of reduced statistical power and false-discovery rates. Safety data collection methods should balance pragmatism and data quality. Overall, understanding statistical considerations is crucial for designing rigorous pragmatic trials that evaluate interventions in elderly populations under real-world conditions. In conclusion, this review focuses on various statistical topics of interest to those designing a pragmatic clinical trial, with consideration of aspects of relevance in the aging research field.

Keywords

pragmatic clinical trials; aged; statistical analysis; geroscience; sample size; secondary data analysis

Subject

Biology and Life Sciences, Aging

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